Thermo-mechanical simulations are central to predicting solidification behaviour, hot‑spot formation, defect evolution and distortions in shape‑casting processes. However, finite‑element–based modelling remains computationally demanding due to mesh‑generation effort, stiff non-linear heat‑transfer equations, and the need to solve PDE residuals at a very high spatial resolution. These challenges limit the use of FEM solvers in rapid design iteration, optimization loops, and real‑time process control. Physics‑informed machine learning (PIML) provides a promising route to alleviate these constraints by embedding governing thermal physics directly into neural‑network training [1]. In this work, we develop a hybrid physics‑informed neural network (PINN) framework tailored to simulating low‑pressure die casting (LPDC) processes used in the manufacture of structural automotive components at Aludyne in Norway. Traditionally run with Finite Element Methods (FEM) the main objective of the simulations is to accurately predict the final shape of the automotive component after casting and compare it to the intended CAD geometry. During each LPDC production cycle, the mould is repeatedly heated, causing it to deform under thermal loading. Consequently, the liquid aluminium solidifies inside a mould that is already distorted, producing a part in an unintended shape. Predicting and correcting these thermally driven shape changes requires a detailed numerical model, and the complexity involved makes vanilla PINNs inadequate [2]. The hybrid PINN formulation incorporates the heat‑transfer Partial Differential Equations, relevant boundary conditions, and temperature‑dependent material behavior along with data from a simplified FEM model into a unified loss function. This enables the model to approximate transient temperature fields and solidification‑front motion while predicting distortions and stresses in the mould and the cast geometry. Predictions from the hybrid PINN framework are validated against FEM results to assess the performance of the proposed approach, and future work will extend this validation to simulations of an actual production part.